You can either publish your package on GitHub (which, in terms of transparency, is always a good idea) or you can submit it to the Comprehensive R Archive Network (CRAN). To make your package easily accessible for everyone, there are two basic strategies. This is why we started working on overviewR. While there are ways to semi-automatically extract this information, we were missing an all-integrated function to do this. In particular, our main motivation came from the need to get an overview of the time and scope conditions (i.e., the units of observations and the time span that occur in the data) as this is a recurring issue both in academic articles and real-world situations. When it comes to our package, we wanted to add an automated way to get an overview - hence the name - of the data you are working with and present it in a neat and accessible way. Second, it is easy to share your code and new functions with others and thereby contribute to the engaged and vivid R community. First, it helps you to approach your problems in a functional way, e.g., by turning your everyday tasks into little functions and bundling them together. Writing a package has two main advantages.

In the following sections, we will use a simplified version of one function ( overview_tab) from our overviewR package as a minimal working example. This tutorial seeks to close this gap: we will provide you with a step-by-step guide - seasoned with new and helpful packages that are also inspired by presentations at the recent virtual European R Users Meeting e-Rum 2020. While there exist many great resources for learning how to write a package in R, we found it difficult to find one all-encompassing guide that is also easily accessible for beginners. That is exactly what we intended with our package overviewR. If you want to contribute to this community, writing a package can be one way. This is to a large part because of the active community that continuously creates and builds extensions for the R world. R is a great resource for data management, statistics, analysis, and visualization - and it becomes better every day.

Create r package